AI models can reason without showing their work — and a new paper suggests we can read that work anyway.
Researchers tested two open-weights frontier models, DeepSeek V3 and Kimi K2, on tasks where the models produce correct answers using content-free filler tokens like dots or counting sequences instead of a visible chain-of-thought. Using attention maps, logit-lens readouts, and a technique called KV-cache transplants, the team traced exactly how the models routed reasoning through those silent positions. They then built an unsupervised decoding pipeline that takes only hidden states as input — no ground-truth labels, no fine-tuning — and recovered intermediate values with 80-95% accuracy across four task types: fact retrieval, numeric composition, string manipulation, and in-context computation.
The finding matters because filler-token reasoning has been treated as a blind spot for AI oversight: if a model produces an answer with no visible reasoning steps, behavioral monitoring has nothing to examine. This paper pushes back on that framing, arguing that monitorability is a property of the full computational trace, not just the tokens a model prints. That's a meaningful shift in how safety researchers might think about what's actually auditable inside a model.
The caveat is that this was demonstrated on specific, bounded tasks with two models — not a general proof that all hidden computation is recoverable. Whether the technique scales to longer reasoning chains or less structured problems is the next question nobody has answered yet.